Recognizing scenes with hierarchical Implicit Shape Models based on spatial object relations for Programming by Demonstration
Recognizing scenes with hierarchical Implicit Shape Models based on spatial object relations for Programming by Demonstration
复制标题
基于空间对象关系的分层隐式形状模型识别场景以进行演示编程
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
R. Dillmann
中科院分区：
文献类型：
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作者：
Pascal Meissner;Reno Reckling;Rainer Jäkel;Sven R. Schmidt;R. Dillmann
我们提出了一种识别场景的方法，包括随时间变化的非结构化室内环境中物体之间的空间关系。对象关系由对象之间的全部六个自由度（DoF）坐标变换表示。当人们演示在给定场景中通常执行的动作时，从视觉上感知到的物体姿势获得它们。我们使用类似于广义霍夫变换的隐式形状模型（ISM）来识别场景。我们将其扩展为考虑对象之间的方向。这包括一个验证步骤，使我们不仅可以推断场景的存在，还可以推断它们组成的对象。ISMs仅限于将场景表示为关系的星形拓扑，这不足以近似复杂动态设置中的对象关系。可能会出现假阳性检测。我们的解决方案是用于识别必须在单独的ISMs中显式表示的对象关系的可交换启发式方法。对象关系由ISMs本身建模。采用层次聚类方法，利用启发式方法构建了ISMs树。单个ISM对场景的学习和识别自然会扩展到多个ISM。
We present an approach for recognizing scenes, consisting of spatial relations between objects, in unstructured indoor environments, which change over time. Object relations are represented by full six Degree-of-Freedom (DoF) coordinate transformations between objects. They are acquired from object poses that are visually perceived while people demonstrate actions that are typically performed in a given scene. We recognize scenes using an Implicit Shape Model (ISM) that is similar to the Generalized Hough Transform. We extend it to take orientations between objects into account. This includes a verification step that allows us to infer not only the existence of scenes, but also the objects they are composed of. ISMs are restricted to represent scenes as star topologies of relations, which insufficiently approximate object relations in complex dynamic settings. False positive detections may occur. Our solution are exchangeable heuristics for recognizing object relations that have to be represented explicitly in separate ISMs. Object relations are modeled by the ISMs themselves. We use hierarchical agglomerative clustering, employing the heuristics, to construct a tree of ISMs. Learning and recognition of scenes with a single ISM is naturally extended to multiple ISMs.